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How IBM Drives Value with Supply-Chain Analytics

IBM continues its journey toward creation of a truly global supply chain, through the use of embedded analytics to gain cross-process visibility. A finalist in the 2013 Supply Chain Innovation Award competition of CSCMP and SupplyChainBrain.

IBM has just been through another one of the major transformations that have marked the company’s history over the decades. In its latest incarnation, the company migrated from a “siloed” organization to one that is “globally integrated,” says Steven Bayline, senior manager, Smarter Analytics, IBM Integrated Supply Chain. The task now is to become a “smarter supply chain.”

Three are three ways that the company can achieve that goal. One is to become a more transparent organization, using descriptive analytics to create end-to-end, near-real-time visibility. The second is to become more predictive, sharpening the ability to understand different scenarios. A third is to become “prescriptive” – meaning that IBM could determine which analytics are most effective in producing the desired outcomes.

The idea, says Bayline, is to “embed analytics into the fabric of supply-chain processes.” Traditionally, analytics has been performed within planning processes on a periodic basis. IBM now wants to take that capability a step further, building analytics into its execution function. It wants to produce reports and generate recommendations directly to users on a day-to-day frequency. Accounts receivable, for example, should be getting daily recommendations on what it needs to be doing with outstanding bills.

Customer segmentation is another key strategy, one that groups accounts by how they behave, and provides the company with a broader base of relevant data so that it can recommend prescriptive actions.

Quality is always an issue. IBM strives to detect quality problems weeks in advance of their occurrence. “We are able to detect trends from multiple data sources, using advanced analytics,” says Bayline.

IBM’s biggest challenge isn’t data quality, however. It’s change management – “getting users to fully embrace it in their business processes,” according to Bayline.

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Keywords: supply chain, supply chain management, change management, customer relationship management, supply chain planning, customer segmentation, supply chain analytics

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